Gráficos CO2/GHG/Pop

---
title: "Gráficos CO2/GHG/Pop"
author: " "
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source_code: embed
    
    theme:
      version: 4
      bootswatch: spacelab
---

```{r setup, include=FALSE}

#Bibliotecas necessárias
library(shiny)
library(flexdashboard)
library(ggplot2)
library(dplyr)
library(plotly)
library(readr)
library(gganimate)
library(tidyverse)
library(lubridate)
library(ggthemes)
library(ggrepel)

#//////////////////////// Chama os arquivos para a montagem dos graficos //////////////////////////////////////

CO2_per_gdp <- read_csv("CO2_per_gdp.csv")

CO2_per_gdp_ppp <- read_csv("CO2_per_gdp_ppp.csv")

GHG_per_gdp <- read_csv("GHG_per_gdp.csv")

GHG_per_gdp_ppp <- read_csv("GHG_per_gdp_ppp.csv")

CO2_per_pop <- read_csv("CO2_per_pop.csv")

GDP_per_POP <- read_csv(("GDP_per_POP.csv"), 
    locale = locale(decimal_mark = ","))

GHG_per_POP <- read_csv("GHG_per_POP.csv")
 
dispersao1 <- read_csv("dispersao1.csv")
dispersao2 <- read_csv("dispersao2.csv")

#//////////////////////// Troca o nome das colunas conforme necessario //////////////////////////////////////

  CO2_per_gdp_ppp <- rename(CO2_per_gdp_ppp, "Year" = Ano)
  
  GHG_per_gdp <- rename(GHG_per_gdp, "country" = contry_name)
  GHG_per_gdp_ppp <- rename(GHG_per_gdp_ppp, "Year" = ano)

  CO2_per_pop <- rename(CO2_per_pop, "country" = "contry_name")
  CO2_per_pop <- rename(CO2_per_pop, "CO2_kg_per_pop" = "CO2_per_pop")
```


# CO2
row {data-width=650} {.tabset}
----------------------------------------------------
### .CO2 Constant GDP {data-padding=20}
```{r}
# Criação do gráfico CO2_per_gdp com as funçoes(ggplot,geom_point,geom_line) e depois transforma em interativo com a funçao ggplotly

 grafico_1 = ggplot(data = CO2_per_gdp, aes(x = year, y = CO2_kg_per_US, group = " " ,color = Country)) +
    geom_point()+
    geom_line( aes(x = year, y = CO2_kg_per_US, color = Country))+
  # Tema do fundo do grafico
    theme_light()+
  # determina a escala dos eixos (x e y)
    scale_x_continuous(breaks = seq(1990,2018))+
    scale_y_continuous(breaks = seq(0,1.5,0.25))+
  # Titulo do grafico e nome dos eixos
    labs(title = "CO2 Emissions x GDP - Cumulative (1990 - 2018)", x = " ",
       y = " CO2 kg per US$")

  # Transforma em interativo
    ggplotly(grafico_1)

```
> Source: World Bank (2021)	/                                                                                                                 Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)

### .CO2 GDP (PPP) {data-padding=20}
```{r}
# Faz a criação do gráfico CO2_per_gdp_ppp com as funçoes(ggplot,geom_point,geom_line) e depois transforma em interativo com a funçao ggplotly

grafico_2 = ggplot(data = CO2_per_gdp_ppp, aes(x = Year, y = CO2_kg_per_ppp_US, group = " " ,color = Country)) +
              geom_point()+
              geom_line( aes(x = Year, y = CO2_kg_per_ppp_US, color = Country))+
            # Tema do fundo do grafico
              theme_light()+
            # determina a escala dos eixos (x e y)
              scale_x_continuous(breaks = seq(1990,2018))+
              scale_y_continuous(breaks = seq(0,2,0.25))+
              labs(title = "CO2 Emissions x GDP (PPP) - Cumulative (1990 - 2018)", 
                 x = " ",
                 y = "CO2 kg per (ppp) US$")

            # Transforma em interativo
             ggplotly(grafico_2)
```
> Source: World Bank (2021)	/                                                                                                                 Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)


# GHG 
row {data-width=350} {.tabset}
-----------------------------------------------------------------------
### .GHG Constant GDP {data-padding=20}
```{r}
# Faz a criação do gráfico GHG_per_gdp com as funçoes(ggplot,geom_point,geom_line) e depois transforma em interativo com a funçao ggplotly 

  grafico_3 = ggplot(data = GHG_per_gdp,aes(x = year, y = GHG_per_GDP_US, group = " " ,color = country)) +
    geom_point()+
    geom_line( aes(x = year, y = GHG_per_GDP_US, color = country))+
  # Tema do fundo do grafico
    theme_light()+
  # determina a escala dos eixos (x e y)
    scale_x_continuous(breaks = seq(1990,2018))+
    scale_y_continuous(breaks = seq(0,2.5,0.25))+
    labs(title = "Greenhouse gas (GHG) emissions x Constant GDP - Cumulative(1990 - 2018)", 
          x = " ",
          y = "kg of equivalent CO2 emission per US$")
 # Transforma em interativo
    ggplotly(grafico_3)
```
> Source: World Bank (2021)	/                                                                                                                 Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)

### .GHG GDP (PPP) {data-padding=20}
```{r}
# Faz a criação do gráfico GHG_per_gdp_ppp com as funçoes(ggplot,geom_point,geom_line) e depois transforma em interativo com a funçao ggplotly 

  grafico_4 = ggplot(data = GHG_per_gdp_ppp, aes(x = Year, y = GHG_per_gdp_ppp_US, group = " " ,color = Country)) +
      geom_point()+
      geom_line( aes(x = Year, y = GHG_per_gdp_ppp_US, color = Country))+
  # Tema do fundo do grafico
      theme_light()+
  # determina a escala dos eixos (x e y)
      scale_x_continuous(breaks = seq(1990,2018))+
      scale_y_continuous(breaks = seq(0, 2.5, 0.25))+
      labs(title = "Greenhouse gas (GHG) emissions x GDP PPP - Cumulative(1990-2018)", 
                    x = " ",
                    y = "[kg of equivalent CO2 emission per (ppp) US$]")
  # Transforma em interativo
      ggplotly(grafico_4)
```
> Source: World Bank (2021)	/                                                                                                                 Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)


#  CO2/GHG per Population
row {data-width=350} {.tabset}
-----------------------------------------------------------------------
### .CO2 per POP {data-padding=20}
```{r}
 # grafico CO2 pela população (a cada 1000 Habitantes)

 grafico_5 = ggplot(data = CO2_per_pop, aes(x = year, y = CO2_kg_per_pop, group = " " ,color = country)) +
      geom_point()+
      geom_line( aes(x = year, y = CO2_kg_per_pop, color = country))+
  # Tema do fundo
      theme_light()+
  # Define as escala dos eixos
      scale_x_continuous(breaks = seq(1990,2018))+
      scale_y_continuous(breaks = seq(0,20,5))+
      labs(title = "CO2 Emissions x Population (1990-2018)", 
                   x = " ",
                    y = "CO2 kg per 1000 hab")
  # Transforma o grafico em interativo
      ggplotly(grafico_5)
```
> Source: World Bank (2021)	/                                                                                                               Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)

### .GHG per POP{data-padding=20}
```{r}
 # grafico CO2 pela população (a cada 1000 Habitantes)
   grafico_5 = ggplot(data = GHG_per_POP, aes(x = year, y = GHG_per_POP, group = " " ,color = country)) +
      geom_point()+
      geom_line( aes(x = year, y = GHG_per_POP, color = country))+
  # Tema do fundo
      theme_light()+
  # Define as escala dos eixos
      scale_x_continuous(breaks = seq(1990,2018))+
      scale_y_continuous(breaks = seq(0,35,5))+
      labs(title = "Greenhouse gas (GHG) emissions x Population (1990-2018)", 
                   x = " ",
                    y = "GHG kg per 1000 hab")
  # Transforma o grafico em interativo
      ggplotly(grafico_5)
```
> Source: World Bank (2021) /	                                                                                                                 Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)


# Dispersão
row {data-width=350} {.tabset}
-----------------------------------------------------------------------
### .(CO2 / GDP) per (CO2 / pop) {data-padding=20}
```{r}

  dispersao1 <- dispersao1 %>%
    filter(dispersao1$year %in% "2018") 

    grafico_7 = ggplot(data = dispersao1, aes(x = CO2_per_pop , y = CO2_per_GDP, color = country)) +
      geom_text(label = dispersao1$country)+
      scale_x_continuous(breaks = seq(0, 25, 2.5))+
      scale_y_continuous(breaks = seq(0, 2, 0.25))+
      theme_bw()+
      labs(title = "CO2 emissions - per population x per GDP(Year: 2018)", 
           x = "CO2 per POP (kg of CO2 emissions per 1000 hab)",
           y = "CO2 per GDP (kg of CO2 emissions per US$)")
    ggplotly(grafico_7)

```
> Source: World Bank (2021) /	                                                                                                                 Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)

### .(GHG / GDP) per (GHG / pop) {data-padding=20}
```{r}

    grafico_8 = ggplot(data = dispersao2, aes(x = GHG_per_POP , y = GHG_per_GDP_US, color = country)) +
      geom_text(label = dispersao2$country)+
      scale_x_continuous(breaks = seq(0, 25, 5))+
      scale_y_continuous(breaks = seq(0, 2, 0.25))+
      theme_bw()+
      labs(title = "Greenhouse gas emissions - per population x per GDP (Year: 2018)", 
                   x = "GHG per POP (kg of equivalent CO2 emissions per 1000 hab) ",
                    y = " GHG per GDP (kg of equivalent CO2 emissions per US$)")
    ggplotly(grafico_8)
    
```
> Source: World Bank (2021) /	                                                                                                                 Authored by: Ministry of Foreign Affairs, SCAEC, Economic Intelligence Center (2021)